def testSvd(self):
n = 100
A = scipy.sparse.rand(n, n, 0.1)
ks = [10, 20, 30, 40]
q = 2
lastError = numpy.linalg.norm(A.todense())
for k in ks:
U, s, V = RandomisedSVD.svd(A, k, q)
nptst.assert_array_almost_equal(U.T.dot(U), numpy.eye(k))
nptst.assert_array_almost_equal(V.T.dot(V), numpy.eye(k))
A2 = (U*s).dot(V.T)
error = numpy.linalg.norm(A - A2)
self.assertTrue(error <= lastError)
lastError = error
#Compare versus exact svd
U, s, V = numpy.linalg.svd(numpy.array(A.todense()))
inds = numpy.flipud(numpy.argsort(s))[0:k*2]
U, s, V = Util.indSvd(U, s, V, inds)
Ak = (U*s).dot(V.T)
error2 = numpy.linalg.norm(A - Ak)
self.assertTrue(error2 <= error)
def profileSvd2(self):
dataDir = PathDefaults.getDataDir() + "erasm/contacts/"
trainFilename = dataDir + "contacts_train"
trainX = scipy.io.mmread(trainFilename)
trainX = scipy.sparse.csc_matrix(trainX, dtype=numpy.int8)
k = 500
U, s, V = RandomisedSVD.svd(trainX, k)
print(s)
print("All done")
def profileSvd2(self):
dataDir = PathDefaults.getDataDir() + "erasm/contacts/"
trainFilename = dataDir + "contacts_train"
trainX = scipy.io.mmread(trainFilename)
trainX = scipy.sparse.csc_matrix(trainX, dtype=numpy.int8)
k = 500
U, s, V = RandomisedSVD.svd(trainX, k)
print(s)
print("All done")
def profileSvd3(self):
dataset = NetflixDataset()
iterator = dataset.getTrainIteratorFunc()
X = iterator.next()
#L = LinOperatorUtils.parallelSparseOp(X)
L = GeneralLinearOperator.asLinearOperator(X)
k = 50
U, s, V = RandomisedSVD.svd(L, k)
print(s)
print("All done")
def profileSvd3(self):
dataset = NetflixDataset()
iterator = dataset.getTrainIteratorFunc()
X = iterator.next()
#L = LinOperatorUtils.parallelSparseOp(X)
L = GeneralLinearOperator.asLinearOperator(X)
k = 50
U, s, V = RandomisedSVD.svd(L, k)
print(s)
print("All done")
#Nystrom method
print("Running Nystrom")
for j, nystromN in enumerate(nystromNs):
omega2, Q2 = Nystrom.eigpsd(L, nystromN)
inds = numpy.flipud(numpy.argsort(omega2))
omega2, Q2 = omega2[inds], Q2[:, inds]
omega2k, Q2k = omega2[0:k], Q2[:, 0:k]
# errors[i, j] += computeBound(L, omega, Q, omega2k, Q2k, k)
errors[i, j] += computeSinTheta(Qkbot, Q2k)
#Randomised SVD method
print("Running Random SVD")
for j, r in enumerate(randSVDVecs):
Q4, omega4, R4 = RandomisedSVD.svd(L, r)
inds = numpy.flipud(numpy.argsort(omega4))
omega4, Q4 = omega4[inds], Q4[:, inds]
omega4k, Q4k = omega4[0:k], Q4[:, 0:k]
# errors[i, j+len(nystromNs)] += computeBound(L, omega, Q, omega4k, Q4k, k)
errors[i, j+len(nystromNs)] += computeSinTheta(Qkbot, Q4k)
#Incremental updates
print("Running Eigen-update")
for j, l in enumerate(IASCL):
omega3, Q3 = eigenUpdate(lastL, L, lastOmegas[j], lastQs[j], l)
inds = numpy.flipud(numpy.argsort(omega3))
omega3, Q3 = omega3[inds], Q3[:, inds]
omega3k, Q3k = omega3[0:k], Q3[:, 0:k]
def clusterFromIterator(self, graphListIterator, verbose=False):
"""
Find a set of clusters for the graphs given by the iterator. If verbose
is true the each iteration is timed and bounded the results are returned
as lists.
The difference between a weight matrix and the previous one should be
positive.
"""
clustersList = []
decompositionTimeList = []
kMeansTimeList = []
boundList = []
i = 0
for subW in graphListIterator:
if __debug__:
Parameter.checkSymmetric(subW)
if self.logStep and i % self.logStep == 0:
logging.debug("Graph index: " + str(i))
logging.debug("Clustering graph of size " + str(subW.shape))
if self.alg!="efficientNystrom":
ABBA = GraphUtils.shiftLaplacian(subW)
# --- Eigen value decomposition ---
startTime = time.time()
if self.alg=="IASC":
if i % self.T != 0:
omega, Q = self.approxUpdateEig(subW, ABBA, omega, Q)
if self.computeBound:
inds = numpy.flipud(numpy.argsort(omega))
Q = Q[:, inds]
omega = omega[inds]
bounds = self.pertBound(omega, Q, omegaKbot, AKbot, self.k2)
#boundList.append([i, bounds[0], bounds[1]])
#Now use accurate values of norm of R and delta
rank = Util.rank(ABBA.todense())
gamma, U = scipy.sparse.linalg.eigsh(ABBA, rank-1, which="LM", ncv = ABBA.shape[0])
#logging.debug("gamma=" + str(gamma))
bounds2 = self.realBound(omega, Q, gamma, AKbot, self.k2)
boundList.append([i, bounds[0], bounds[1], bounds2[0], bounds2[1]])
else:
logging.debug("Computing exact eigenvectors")
self.storeInformation(subW, ABBA)
if self.computeBound:
#omega, Q = scipy.sparse.linalg.eigsh(ABBA, min(self.k2*2, ABBA.shape[0]-1), which="LM", ncv = min(10*self.k2, ABBA.shape[0]))
rank = Util.rank(ABBA.todense())
omega, Q = scipy.sparse.linalg.eigsh(ABBA, rank-1, which="LM", ncv = ABBA.shape[0])
inds = numpy.flipud(numpy.argsort(omega))
omegaKbot = omega[inds[self.k2:]]
QKbot = Q[:, inds[self.k2:]]
AKbot = (QKbot*omegaKbot).dot(QKbot.T)
omegaSort = numpy.flipud(numpy.sort(omega))
else:
omega, Q = scipy.sparse.linalg.eigsh(ABBA, min(self.k2, ABBA.shape[0]-1), which="LM", ncv = min(10*self.k2, ABBA.shape[0]))
elif self.alg == "nystrom":
omega, Q = Nystrom.eigpsd(ABBA, self.k3)
elif self.alg == "exact":
omega, Q = scipy.sparse.linalg.eigsh(ABBA, min(self.k1, ABBA.shape[0]-1), which="LM", ncv = min(15*self.k1, ABBA.shape[0]))
elif self.alg == "efficientNystrom":
omega, Q = EfficientNystrom.eigWeight(subW, self.k2, self.k1)
elif self.alg == "randomisedSvd":
Q, omega, R = RandomisedSVD.svd(ABBA, self.k4)
else:
raise ValueError("Invalid Algorithm: " + str(self.alg))
decompositionTimeList.append(time.time()-startTime)
if self.alg=="IASC":
self.storeInformation(subW, ABBA)
# --- Kmeans ---
startTime = time.time()
inds = numpy.flipud(numpy.argsort(omega))
standardiser = Standardiser()
#For some very strange reason we get an overflow when computing the
#norm of the rows of Q even though its elements are bounded by 1.
#We'll ignore it for now
try:
V = standardiser.normaliseArray(Q[:, inds[0:self.k1]].real.T).T
except FloatingPointError as e:
logging.warn("FloatingPointError: " + str(e))
V = VqUtils.whiten(V)
if i == 0:
centroids, distortion = vq.kmeans(V, self.k1, iter=self.nb_iter_kmeans)
else:
centroids = self.findCentroids(V, clusters[:subW.shape[0]])
if centroids.shape[0] < self.k1:
nb_missing_centroids = self.k1 - centroids.shape[0]
random_centroids = V[numpy.random.randint(0, V.shape[0], nb_missing_centroids),:]
centroids = numpy.vstack((centroids, random_centroids))
centroids, distortion = vq.kmeans(V, centroids) #iter can only be 1
clusters, distortion = vq.vq(V, centroids)
kMeansTimeList.append(time.time()-startTime)
clustersList.append(clusters)
#logging.debug("subW.shape: " + str(subW.shape))
#logging.debug("len(clusters): " + str(len(clusters)))
#from apgl.util.ProfileUtils import ProfileUtils
#logging.debug("Total memory usage: " + str(ProfileUtils.memory()/10**6) + "MB")
if ProfileUtils.memory() > 10**9:
ProfileUtils.memDisplay(locals())
i += 1
if verbose:
return clustersList, numpy.array((decompositionTimeList, kMeansTimeList)).T, boundList
else:
return clustersList
times[i, 0] = time.time() - startTime
errors[i, 0] = numpy.linalg.norm(X - (U2*s2).dot(V2.T))
#Now RSVD + update
if i == 0:
startTime = time.time()
U3, s3, V3 = sppy.linalg.core.rsvd(X, k,q=q)
times[i, 1] = time.time() - startTime
lastX = X
else:
E = X - lastX
E.eliminate_zeros()
print(X.nnz, E.nnz)
startTime = time.time()
U3, s3, V3 = RandomisedSVD.updateSvd(X, U3, s3, V3, E, k, p)
times[i, 1] = time.time() - startTime
lastX = X
errors[i, 1] = numpy.linalg.norm(X - (U3*s3).dot(V3.T))
#Accurate method
startTime = time.time()
U4, s4, V4 = SparseUtils.svdPropack(X, k)
times[i, 2] = time.time() - startTime
errors[i, 2] = numpy.linalg.norm(X - (U4*s4).dot(V4.T))
#Final method - just use the same SVD
if i == 0:
